CVD Prediction Project
Author: Julian Borges, M.D.
Date: August 21, 2024

Project Overview
This project explores the relationship between early-stage diabetes symptoms and cardiovascular disease (CVD) risk using machine learning techniques. The goal is to develop predictive models that can aid in the early detection of heart failure risk among diabetic patients.

Contents
This zip file contains the following:

CVD_Prediction_Project.Rmd: The R Markdown file used for analysis and generating the report.
CVD_Prediction_Project.R: The standalone R script for running the analysis.
CVD_Prediction_Project.pdf: The knitted PDF report generated from the R Markdown file.
Datasets/:

execute the script ywill it auto download

diabetes_data_upload.csv: The dataset containing early-stage diabetes risk prediction data.

heart_failure_clinical_records_dataset.csv: The dataset containing clinical records of heart failure patients.

CVD_Prediction_Manuscript.pdf: The full manuscript that details the study, methods, results, and discussion.

How to Use

Running the Analysis:

You can run the analysis by either executing the CVD_Prediction_Project.R script in R or by opening the CVD_Prediction_Project.Rmd file in RStudio and knitting it to generate a report.
Data:

The datasets used in this analysis are included in the Datasets/ folder. The data has been preprocessed as described in the manuscript.
Manuscript:

The manuscript file CVD_Prediction_Manuscript.pdf provides a comprehensive overview of the study, including background, methodology, results, and conclusions.
Contact
For any questions regarding this project, please contact Julian Borges at fxmedbrasil@gmail.com